Scientists Invent Imaging Method to Assess Quality of 3D Printed Metal Parts

Analyzing unique crystal patterns on the surface of a 3D printed metal can pave the way for certification and quality assessment of parts made by additive manufacturing. Credit: Nanyang Technological University

Scientists at Nanyang Technological University, Singapore (NTU Singapore) have developed a fast and inexpensive imaging method that can analyze the structure of 3D printed metal parts and offer insight into material quality.

Most 3D printed metal alloys are made up of a myriad of microscopic crystals, which differ in shape, size, and atomic lattice orientation. By mapping this information, scientists and engineers can infer properties of the alloy, such as strength and toughness. It comes down to looking at the grain of the wood, where the wood is strongest when the grain is continuous in the same direction.

This new technology made in NTU could benefit, for example, the aerospace sector, where the rapid and low-cost evaluation of mission-critical parts (turbine, fan blades and other components) could be a game-changer for the industry of maintenance, repair and overhaul. .

Until now, analysis of this “microstructure” in 3D-printed metal alloys has been achieved through laborious and time-consuming measurements using scanning electron microscopes, which range in price from 100,000 to 2 million Singapore dollars.

The method devised by Nanyang Assistant Professor Matteo Seita and his team provides the same quality of information in minutes using a system consisting of an optical camera, a flashlight and a laptop computer that runs a proprietary machine learning software. developed by the team, with the hardware costing around S$25,000.

The team’s new method requires first treating the metal surface with chemicals to reveal the microstructure, then placing the sample facing the camera and taking multiple optical images as the flashlight illuminates the metal in different directions.

The software then analyzes the patterns produced by the light reflected on the surface of different metallic crystals and deduces their orientation. The whole process takes about 15 minutes.

The team’s findings have been published in the peer-reviewed scientific journal npj Computer materials.

“Thanks to our fast and inexpensive imaging method, we can easily distinguish good 3D printed metal parts from defective ones. Currently, it is impossible to tell the difference unless we thoroughly assess the microstructure of the material”, says Assistant Professor Seita, from NTU’s School of Mechanical and Aerospace Engineering and School of Materials Science and Engineering.

“Two 3D printed metal parts are not created equal, even though they may have been produced using the same technique and have the same geometry. Conceptually, this is akin to how two otherwise identical wooden artifacts can each possess a different grain structure.”

A new imaging method, a boost for certification and quality assessment of 3D printing

Assistant Professor Seita believes their innovative imaging method has the potential to simplify the certification and quality assessment of metal alloy parts produced by 3D printing, also known as additive manufacturing.

One of the most common techniques used to 3D print metal parts uses a high-powered laser to melt metal powders and fuse them together, layer by layer, until the complete product is printed.

However, the microstructure and therefore the quality of the resulting printed metal depends on several factors, including the speed or intensity of the laser, the time allowed for the metal to cool before the next layer is melted, and even the type and brand. . metal powders used.

This is why the same design printed by two different machines or a production house can result in parts of different quality.

Instead of using a complicated computer program to measure the orientation of the crystal from the acquired optical signals, the “intelligent software” developed by Professor Asst Seita and his team uses a neutral network, mimicking the way the human brain forms associations and processes thought. The team then used machine learning to program the software, feeding it hundreds of optical images.

Eventually, their software learned to predict the orientation of crystals in the metal from the images, based on differences in the scattering of light on the surface of the metal. It was then tested to be able to create a complete “crystal orientation map”, which provides complete information about the shape, size and orientation of the crystal’s atomic lattice.

Adjustment of alloy microchemistry for flawless metal 3D printing

More information:
Mallory Wittwer et al, A Machine Learning Approach to Map Crystal Orientation Using Light Microscopy, npj Computer materials (2022). DOI: 10.1038/s41524-021-00688-1

Provided by Nanyang Technological University

Quote: Scientists Invent Imaging Method to Assess Quality of 3D Printed Metal Parts (2022, Feb 25) Retrieved Feb 26, 2022 from -quality-3d-printed.html

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